To further efficiency in modem food product processing and packaging operations, and in food portion control, efforts have been made to replace formerly manually conducted operations with automated procedures and methods. Such methods and products are particularly desirable in quality assurance operations and procedures to ensure that regulated and mandated quality standards are consistently adhered to throughout production operations.
For example, in U.S. patent application publication No.: U.S. 2003/0024744 (Feb. 6, 2003) to Ring, there is disclosed a data acquisition and/or control method and device which employs a conveyor weigh scale, or “weigh scale control”, which is said to automatically determine a crucial sample period for accurately weighing various food products. The described method also employs an algorithm for data acquisition and control in a food product weighing operation. In this method, a conveyor weigh scale senses a dynamic weight of a product as it passes over a weigh scale, which can be expressed as a weight waveform of sensed weight over time as the product passes over the scale. As described, an accurate weight reading for the moving product is made during a brief sample period within the waveform where weight readings are most constant and representative of the static weight of the product. This method is said to be an advantage over conventional continuously moving product scales which use laser sensor or photosensitive components, such as an optical or other external triggering device. These devices are used to detect the entry of a product into a weigh scale, and then actuate the scale which uses fixed timing numbers to estimate the position of the sample period on a weight waveform to make weight measurements.
The improvement associated with this method is said to be the provision of a software algorithm for a weight scale associated with a continuously moving conveyor which is capable of positioning the sample period on each product weight waveform and in which the weight and speed of the product passing over the scale does not affect the positioning of the sample period. The algorithm calculates the sample period using waveform slope characteristics.
The weight measurement method described above is also said to be useful with such conventional food processing methods, such as illustrated, for example, in U.S. Pat. Nos. 5,704,265; 5 and 5,724,874 which is a slicing machine with a conveyor drive/classifier system that is responsive to a weigh scale to direct products within a weight tolerance to an “accept conveyor, and out-of-weight tolerance products to a “reject” conveyor. The slicing machine produces a series of stacks of food loaf slices which move outwardly of the machine on an input conveyor which, as described, continuously senses the weight of the sliced product appearing on the scale, which, in turn, outputs a continuous succession of weight readings of samples at regular time intervals to define corresponding waveforms, and which are characterized as dynamic weight measures of product. The assemblage enables rapid weight measurement on the order of five-hundred samples per second, with a rapid conveyor product speed of over one-hundred product stacks per minute. The system is applicable to all different types of commercial food product loaves, such as ham, beef, pork, fish in a variety of shapes and sizes, and in differently shaped stacks of food product.
Other conventional food processing measurement systems include two-dimensional (2-D) imaging systems to determine length and width, and used, for example, in oyster measurements and in sizing other food objects. These systems typically produce a 2-D image which corresponds to an amount of light and corresponding current, which is picked up by pixels of a charge-coupled device (CCD) camera, and which is positioned to receive images from a particular area. These systems are also able to obtain individual weight data per product, such as the weight of an oyster, by correlating a sample group weight of food products with pixel data using an equation relating to 2-D image and volume.
Further refinements to such methods of determining food product volume employ three-dimensional (3-D) optical volume measurement such as disclosed in U.S. Pat. No. 6,369,401 to Lee. In this method, one or more lines of radiation are projected from a radiation source, such as a laser light source, onto a food object, and thereafter detecting lines of radiation reflected from the object. Reflected radiation is compared with that reflected from a reference surface to determine the height, length and width of an object at a location corresponding to at least one line of radiation impinging on the object. As further disclosed in this method, several laser lines are impinged onto a surface area on which a food product object is located, and onto a reference surface of which no food product object is located. A light sensitive device, such as a camera, having a plurality of pixel elements that can receive light from a plurality of surface locations, which is light reflected from the food product object, or surface, is used to determine light intensity received, and displacement of laser lines relative to a reference location.
Raw image data from the camera is received by a central processing unit (CPU), which determines the binary image of projected area to determine length and width dimensions. The CPU also uses laser line displacement data to determine object height at the various locations of the object, all of which data is then used by the CPU to calculate the product or object volume.
Another food product data processing/process control system and method is discloses in International Patent Application No. PCT/GB99/00766 to Whitehouse. In this system, a food product traverses an inspection region on a conveyor belt, and a transducer determines shape; size and cross-section of the product in the inspection region. Data generating transducers can be rotated about an object or product to be measured so as to inspect the whole of the product surface for accurate size and shape measurements, with signals generated when a length of product enters and leaves an inspection region, and with computation means capability to produce product arrival and product departure signals.
As also disclosed, data generating laser displacement transducers may be mounted in a ring pattern around or at an inspection site or region, and situated to direct their beams through a gap between two in-line conveyors, with the ring being driven by a servo motor, and with output data of the transducers logged by a computer means.
In yet another example of conventional product characteristic data gathering in commercial food processing techniques, U.S. Patent application publication No. U.S. 2002/0014444 (Feb. 7, 2002) to Hebrank describes a method and apparatus for automated poultry egg classification. A conveying system is used transport eggs to an inspection station where, among other characteristics, egg temperature is measured by a thermal codling system which measures temperature by detecting corresponding infrared radiation, thereby generating corresponding signals which are sent to a controller, or CPU.
Currently, in conventional poultry, meat and processed food plant operations in general, quality control and assurance techniques are oftentimes labor intensive. For example, in a typical poultry plant operation, a sample of all boneless breast meat product is tested for size, weight, temperature and other characteristics and/or defects or standard deviations by method(s) which require at least some aspect of manual labor or exertion to produce measurable data, e.g. a quality assurance data point. Usually, to obtain weight measurements, an employee is required to extract a sample of product from a product process line and place it on a scale for weighing. The product weight can then be recorded in a log, or other database, such as a computer database program.
Product thickness, or other dimensions of width and length, are also typically manually measured, such as, for example, by using calipers, which data is also manually logged, or otherwise fed to a database. The temperature of each product sample is also manually checked and recorded. Such labor intensive efforts are undesirable in that up to two minutes or more is required to check each product sample, thereby resulting in significantly less data generated than if performed by automatic machine means. Additionally, such human intervention with quality assurance checking procedures invariably results in inconsistent or even fabricated data generation leading to unnecessarily unreliable quality assurance measures.
It is therefore desirable, and an object of the present invention, to provide a completely automated method and system to generate all data contemplated as required for any food product processing quality assurance program or other product standardization or portion control operation.